Method and device for selecting a fingerprint image in a sequence of images for authentication or identification

ABSTRACT

A method for selecting an image of a fingerprint for identifying an individual is described. The method includes: acquiring a current image comprising a fingerprint and segmenting said fingerprint; determining a value representing a stability of said current image; determining a value representing the sharpness of said current image; determining a score, said score being a combination of said value representing a stability, of said value representing a sharpness and of a number of segmented fingerprints; and selecting said current image for identifying said individual in the case where said score is higher than a first threshold value, and otherwise storing said current image in memory as best image in the case where the score thereof is higher than a best score value, and repeating this method.

TECHNICAL FIELD

The invention relates to a method and device for selecting an image of afingerprint for identifying or authenticating an individual. The imageis more particularly selected from a sequence of images acquired by animage capture device.

PRIOR ART

The use of fingerprints, for example of the type consisting of a printof a finger or of a plurality of fingers, makes it possible to secureaccess to buildings or to machines. Using this technology makes itpossible to reinforce security since the probability of two personshaving two identical fingerprints is almost zero.

A system for capturing fingerprints makes it possible to obtain at leastone image of at least one fingerprint. In the case of an identification,each print is compared with a set of reference fingerprints contained ina database. In the case of an authentication, each print is comparedwith a single fingerprint. The comparison makes it possible to determinewhether each fingerprint obtained belongs or not to a person referencedin the database or whether the person is indeed the one that they claimto be.

For this purpose, using mobile equipment, e.g. a smartphone, in order tocapture one or more fingerprints of an individual in order to identifyor authenticate them is known. A print capture system can be added tothe mobile equipment when it is manufactured. Such a system forcapturing prints on a smartphone often has an acquisition surface lessthan the size of the print. It therefore requires the use of adaptedimage mosaicing algorithms in order to reconstruct the print in itsentirety. Such a solution is therefore expensive to implement. Themajority of such mobile equipment being equipped with a colour camera,the document of Lee et al entitled “Image Preprocessing of FingerprintImages Captured with a Mobile Camera” published in 2006, in ICB(“International Conference on Advances in Biometrics”) suggests usingthis camera directly for developing a print recognition system.

However, such an approach poses numerous problems compared with theconventional print capture systems. In particular, the backgrounds ofthe images of a finger or fingers captured are highly variable (e.g.ground, landscapes, etc.). The quality of the images acquired is alsovery variable from one item of mobile equipment to another.

It is desirable to overcome these drawbacks of the prior art. It is inparticular desirable to propose a method and a device that make itpossible to use any type of image sensor present on the consumer mobileequipment in order to acquire at least one good-quality fingerprint in asequence of images. Moreover, this method must be quick to implement anduse little memory.

DISCLOSURE OF THE INVENTION

A method for selecting an image of a fingerprint for identifying orauthenticating an individual is described. The image being selected in asequence of images acquired by an image capture module, the methodcomprises:

a) acquiring an image, referred to as the current image, comprising atleast one fingerprint with said image capture module and segmenting saidat least one fingerprint;b) determining a value representing a stability of said current imagefrom said at least one segmented fingerprint;c) determining a value representing the sharpness of said current imagefrom said at least one segmented fingerprint;d) determining a score for said current image, said score being acombination of said value representing a stability, of said valuerepresenting a sharpness and of a number of segmented fingerprints insaid current image;e) selecting said current image for identifying or authenticating saidindividual in the case where said score is higher than a first thresholdvalue, and otherwise storing said current image in memory as best imagein the case where the score thereof is higher than a best score value,said best score value then being made equal to the score of said currentimage, and repeating steps a) to e).

The method described advantageously makes it possible to acceleratecontrol of access to buildings, e.g. to stadiums, in order in particularto combat fraud. This is because the method improves the precision ofidentification and authentication and reduces the acquisition time witha negligible increase in the computing time.

According to a particular embodiment, determining a value representing astability of said current image from said at least one segmentedfingerprint comprises:

-   -   for each segmented fingerprint, said fingerprint being segmented        in the form of a region, referred to as the current region:    -   determining a centre of gravity of said current region;    -   determining a stability value of said current region according        to a criterion of spatial proximity between said current region        and determined regions in f images preceding said current image        timewise;    -   determining a mean value of said determined stability values,        said value representing a stability of said current image being        equal to said mean value determined.

According to a particular embodiment, determining a stability value ofsaid current region comprises:

-   -   determining a distance between the centre of gravity of said        current region and the centre of gravity of at least one        determined region in one of the f images preceding said current        image timewise, referred to as the preceding region, with which        a label is associated;    -   associating said label with said current region in the case        where said distance is smaller than a second threshold value;        and    -   associating a new label with said current region otherwise;    -   determining the percentage of occurrences in F images preceding        said current image of said label, said stability value of said        current region being equal to said percentage.

According to a variant embodiment, determining a stability value of saidcurrent region comprises:

-   -   determining a distance between the centre of gravity of said        current region and the centre of gravity of at least one        determined region in one of the f images preceding said current        image timewise, referred to as the preceding region, with which        a label is associated;    -   determining a first directing vector of said current region and        a second directing vector of said preceding region;    -   associating said label with said current region in the case        where said distance is less than a second threshold value and a        scalar product of said first directing vector and of said second        directing vector is higher than a third threshold value;    -   associating a new label with said current region otherwise;    -   determining the percentage of occurrences in F images preceding        said current image of said label, said stability value of said        current region being equal to said percentage.

According to a particular feature, f=0.2*fps and F=0.8*fps where fps isthe number of images per second.

According to a particular embodiment, determining a value representingthe sharpness of said current image from said at least one segmentedfingerprint comprises:

-   -   determining gradients in said current image;    -   for each segmented fingerprint, said fingerprint being segmented        in the form of a region, determining a sum of the magnitudes of        the gradients inside said region;    -   determining a mean value of said sums determined, said value        representing the sharpness of said current image being equal to        said mean value determined.

According to a particular embodiment, each region is eroded prior tosaid determination of a sum of the magnitudes of the gradients and saiddetermination of a sum of the magnitudes of the gradients comprisesdetermining a sum of the magnitudes of the gradients inside said erodedregion.

According to a particular embodiment, said score for said current imageis determined according to the following formula:

Score(Im _(t))=min(TH1,Stability(Im _(t)))*min(TH2,Sharpness(Im_(t)))*min(TH3,Nt)

where TH1, TH2 and TH3 are threshold values, Stability(Im_(t)) is thestability value of said current image, Sharpness(Imt) is the sharpnessvalue of said current image and Nt is the number of segmentedfingerprints in said current image.

According to a particular embodiment, the step e) further comprisesupdating said best score value of said image stored in memory as bestimage taking account of the value representing the stability of saidcurrent image;

and selecting said image stored in memory as best image for identifyingand authenticating said individual in the case where said updated bestscore value is higher than said first threshold value and than saidscore of said current image.

According to a particular embodiment, updating said best score value ofsaid image stored in memory as best image taking account of the valuerepresenting the stability of said current image comprises, for eachregion that said best image shares with said current image, replacingthe stability associated with said region in said best image with thestability associated with said region in said current image in the casewhere the stability associated with said region in said current image isgreater than the stability associated with said region in said bestimage and updating said best score value of said best image takingaccount of said replaced stabilities.

A device for selecting an image of a fingerprint for identifying orauthenticating an individual is also described. The selection devicecomprises an image capture module, said image being selected in asequence of images acquired by said image capture module. The selectiondevice comprises electronic circuitry configured for:

a) acquiring an image, referred to as the current image, comprising atleast one fingerprint with said image capture module and segmenting saidat least one fingerprint;b) determining a value representing a stability of said current imagefrom said at least one segmented fingerprint;c) determining a value representing the sharpness of said current imagefrom said at least one segmented fingerprint;d) determining a score for said current image, said score being acombination of said value representing a stability, of said valuerepresenting a sharpness and of a number of segmented fingerprints insaid current image;e) selecting said current image for identifying or authenticating saidindividual in the case where said score is higher than a first thresholdvalue, and otherwise storing said current image in memory as the bestimage in the case where the score thereof is higher than a best scorevalue, said best score value then being made equal to the score of saidcurrent image, and repeating steps a) to e).

A computer program product is described. It comprises instructions forimplementing, by a device for selecting an image, the method accordingto one of the preceding embodiments, when said program is executed by aprocessor of said device for selecting an image.

A storage medium is described. It stores a computer program comprisinginstructions for implementing, by a device for selecting an image, themethod according to one of the preceding embodiments, when said programis executed by a processor of said device for selecting an image.

BRIEF DESCRIPTION OF THE DRAWINGS

The features of the invention mentioned above, as well as others, willemerge more clearly from the reading of the following description of anexample embodiment, said description being made in relation to theaccompanying drawings, among which:

FIG. 1 illustrates schematically a method for selecting an image of afingerprint for identifying or authenticating an individual according toa particular embodiment;

FIG. 2 illustrates schematically an example of performance of a step ofdetermining a value representing a stability of an image according to aparticular embodiment;

FIG. 3 illustrates schematically a method for associating regions witheach other according to a particular embodiment;

FIG. 4 illustrates schematically an example of performance of a step ofdetermining a value representing a sharpness of an image according to aparticular embodiment; and

FIG. 5 illustrates schematically an example of hardware architecture ofa device for selecting an image of a fingerprint for authentication oridentification.

DETAILED DISCLOSURE OF EMBODIMENTS

FIG. 1 illustrates schematically a method for selecting an image of afingerprint for identifying or authenticating an individual, said imagebeing selected from a sequence of images acquired by an image capturedevice according to a particular embodiment. The image capture device isadvantageously a camera of an item of consumer mobile equipment, e.g. asmartphone, a tablet, a fixed or portable computer, etc. Hereinafter,the terms «print» and “fingerprint” are considered to be synonyms.

The method starts at the step S100, at an instant t. During a step S102,an image Im_(t) comprising at least one fingerprint of the individual isacquired with an image sensor. The individual positions their hand infront of the sensor so that these prints are visible, the hand beingstationary or in movement. At least one fingerprint is segmented in theimage Im_(t) in the form of a region ROI_(n), n is an index serving toidentify the region. In general, Nt fingerprints are segmented in saidimage with Nt between 1 and 5.

By way of simple illustrative examples, the finger segmentation methoddescribed in the document of Lee et al entitled “Preprocessing of aFingerprint Image Captured with a Mobile Camera”, published in 2006 inInternational Conference on Biometrics (ICB) 348-355, makes it possibleto obtain segmented fingerprints. The method described in the documentof Suzuki et al entitled “Topological structural analysis of digitizedbinary images by border following” published in 1985 in Computer Vision,Graphics, and Image Processing, 30(1):32-46, makes it possible to obtaincontours of fingerprints from segmented fingerprints. The methoddescribed in the document by Ramer et al entitled “An iterativeprocedure for the polygonal approximation of plane curves” published in1972 in Computer Graphics and Image Processing, 1(3), 244-256, makes itpossible to obtain polygons from said fingerprint contours.

It should however be noted that the embodiments described are notlimited solely to this finger segmentation method. Other methods can beused. Thus segmentation methods based on deformable models usingparametric curves (e.g. first-degree splines) can be used.

Each region ROI_(n), with n varying from 0 to Nt−1, is for exampleidentified by means of a convex polygon with m vertices (xi, yi) with minteger and (xi, yi) are the coordinates of a vertex of index i, ivarying from 0 to m, with x0=xm and y0=ym. The vertices corresponding tothe edge of the image or to the cutting line separating the fingerprintfrom the intermediate phalanx are identified with a specific labelduring the segmentation step.

In a step S104, a value representing a stability of the image Im_(t) isdetermined. A particular embodiment of the step S104 is illustratedschematically by FIG. 2.

In a step S106, a value representing a sharpness of the image Im_(t) isdetermined. A particular embodiment of the step S106 is illustratedschematically by FIG. 4.

In a step S108, a score is determined for the image Im_(t), the scorebeing a combination of the values representing the stability andsharpness of the number Nt of segmented fingerprints in the imageIm_(t). The score represents the quality of the image. The higher thescore, the higher the quality of the image and therefore better will bean identification/authentication made from this image. In oneembodiment, the score Score(Im_(t)) associated with the image Im_(t) iscalculated as follows:

Score(Im _(t))=fct(Stability(Im _(t)))*g(Sharpness(Im _(t)))*h(Nt)

where fct, g and h are functions. For example, fct, g and h areidentical and are equal to the identity function. In a variant,fct(Stability(Im_(t)))=min(THS, Stability(Im_(t))),g(Sharpness(Im_(t)))=min (THN, Sharpness(Im_(t))) and h(Nt)=min(THNt,Nt) where THS, THN and THNt are predefined threshold values. Forexample, THS=0.67, THN=1500 and THNt=2

In a step S110, the score Score(Im_(t)) is compared with a thresholdvalue TH. For example, TH=THS*THN*THNt. The value TH is fixed accordingto a compromise between image quality and speed of execution of themethod. If the score associated with the image Im_(t) is higher than orequal to TH, then the image is considered to be of sufficiently goodquality to be selected during a step S112 for the purpose ofidentifying/authenticating the individual to which it belongs. In thecontrary case, i.e. Score(Im_(t))<TH, the method resumes at the stepS102 and a new image is acquired.

In an optional variant embodiment, at each iteration, the best imageacquired up until now, i.e. the one with the highest score, is stored inmemory. Thus, if Score(Im_(t))<TH, the score Score(Im_(t)) is, in a stepS114, compared with the highest score obtained up until now and denotedScoreMax. If Score(Im_(t))>ScoreMax then, in a step S116, ScoreMax ismade equal to Score(Im_(t)) and Im_(t) is stored in memory as being thebest image, denoted BestImage for an identification/authentication.Where applicable, the image previously stored as being the best image istherefore deleted. In another optional variant embodiment, at each newiteration, the stability and therefore the score of the image BestImageis updated taking account of information on the stability coming fromimages acquired subsequently to the best image.

This is because the concept of stability unduly penalises certainpotentially sharp images with correctly segmented fingerprints. Forexample, in a series of n perfectly stable images Im_(t), Im_(t+1), . .. Im_(t+n), for an equal number of segmented fingerprints and an equalmeasurement of sharpness, the score of the last image Score(Im_(t+n)) ishigher than Score(Im_(t)) since the value representing a stability ishigher for the image Im_(t+n) than for the image Im_(t). However, theseare the same images. Updating the score of the best image at eachiteration taking account of information on the stability coming fromimages acquired subsequently to the best image makes it possible tocompensate for this effect. Take a sequence of images Im₀, Im₁, . . .Im_(t′), . . . , Im_(t) acquired by the image sensor. Let Im_(t′) be thebest current image that was acquired at the instant t′. Its stabilityvalue is updated taking account of the information on the stability ofimages acquired subsequently to t′. The score of Im_(t′) after theupdating, is once again compared with TH. If the updated score is higherthan TH, then the method continues at the step S112 with Im_(t′). Thisapproach makes it possible to reduce the mean time for selecting animage for identification/authentication since the updated scoreScore(Im_(t′)) is potentially higher than before the updating and mayequal or exceed the threshold TH.

In a particular embodiment, the updating of the stability and thereforeof the score of the image Im_(t′), (the current best image) is performedeven in the case where the score associated with the image Im_(t) ishigher than or equal to TH. This is because it is possible that,following this updating, the score of the image Im_(t′) is in the endhigher than the score of the image Im_(t) and therefore than TH. In thiscase, it is preferable to select the image Im_(t′) that is of betterquality than the image Im_(t).

In another particular embodiment, an additional stop criterion is usedduring the step S110. If the number of images acquired is greater than athreshold value then BestImage is selected for identifying orauthenticating the individual. Thus, after a certain acquisition time,if no image has been found with a Score higher than TH, the best imagethat is stored in memory is used.

FIG. 2 illustrates schematically an example of performance of the stepS104.

The step S104 starts (S104-2) with n=0. This is a simple convention.

During a step S104-4, the centre of gravity of the region ROI_(n) isdetermined. The coordinates (cx, cy) of the centre of gravity are forexample determined as follows:

${c_{x} = {\frac{1}{6*A}{\sum\limits_{i = 0}^{m - 1}{\left( {x_{i} + x_{i + 1}} \right)*\left( {{x_{i}*y_{i + 1}} - {x_{i + 1}*y_{i}}} \right)}}}}{c_{y} = {\frac{1}{6*A}{\sum\limits_{i = 0}^{m - 1}{\left( {y + y_{i + 1}} \right)*\left( {{x_{i}*y_{i + 1}} - {x_{i + 1}*y_{i}}} \right)}}}}$

where: A=½Σ_(i=0) ^(m-1)(x_(i)*y_(i+1)−x_(i+1)*y_(i)), xm=x0 and ym=y0.

In a step S104-6, a stability value is determined for the regionROI_(n). The stability value for the region ROI_(n) of the image Im_(t)is equal to a percentage of occurrences, in F images preceding the imageIm_(t), of a label that is associated therewith, F being a positiveinteger representing a number of images. F is proportional to the imagefrequency fps, e.g. F=0.8*fps. For example, F=20 when fps is equal to 25images per second.

For this purpose, the region ROI_(n) is compared with segmented regionsROI_(p) in f images Im_(t′) that precede it, f being a positive integerrepresenting a number of images, e.g. f=5 for a frequency of 25 imagesper second. More generally, f is proportional to the image frequency,e.g. f=0.2*fps. The objective of this step is to associate, with theregion ROI_(n), a label of a region already labelled previously takingaccount of a spatial proximity criterion or where applicable associatinga new label in the case where this spatial proximity criterion is notsatisfied with any region of the preceding images.

This step is illustrated in FIG. 3. The value t′ is initialised (S300)to the value t−1 and p to the value 0.

In a step S302, a spatial distance between the regions ROI_(n) andROI_(p) is compared with a threshold d. By way of example, the spatialdistance is equal to the Euclidean distance ∥{right arrow over(CG_(n,t)CG_(p,t′))} ∥_(L2) where CG_(n,t) is the centre of gravity ofthe region ROI_(n) in the image Im_(t) and CG_(p,t′) is the centre ofgravity of the region ROI_(p) in the image Im_(t′) preceding the imageIm_(t). Other distances can be used.

For example, d=100 for images of size 2000×2000 pixels. This proximitycriterion makes it possible to verify that the region ROI_(n) has notdrifted excessively between the instant t′ and the instant t.

If ∥{right arrow over (CG_(n,t)CG_(p,t′))} ∥_(L2)<d then the regionROI_(n) the image Im_(t) is associated with the region ROI_(p) in theimage Im_(t′), i.e. the label ROI_(p) is associated with ROI_(n) (stepS304). The region ROI_(p) will, where applicable, have been associatedwith a region ROI_(q) in an image In_(t″) that precedes Imt′. All theregions thus associated with each other will have the same label, alabel being created at the time of the first occurrence.

In a particular embodiment, if ∥{right arrow over (CG_(n,t)CG_(p,t′))}∥_(L2)<d, the orientation of the region ROI_(n) in the image Im_(t) iscompared with the orientation of the region ROI_(p) in the imageIm_(t′). For this purpose, a directing vector is determined for each ofthe two regions. This directing vector is defined by the centre ofgravity of the region and the central point of the cutting line definingthe bottom of the region during the segmentation step. In the case wherethe scalar product PS of the two directing vectors is above apredetermined threshold R (step S303), the region ROI_(n) in the imageIm_(t) is associated with the region ROI_(p) in the image Im_(t′), i.e.the label of ROI_(p) is associated with ROI_(n). If the scalar productis less than R, then the two regions are not associated. For example R≥0will be selected, or according to another example R≥0.5. For example,when R=0, if the scalar product is positive or zero, the region ROI_(n)in the image Im_(t) is associated with the region ROI_(p) in the imageIm_(t′), i.e. the label of ROI_(p) is associated with ROI_(n). If thescalar product is negative, then the two regions are not associated.This has in particular the effect of eliminating any false detections ofobjects located in the background.

If ∥{right arrow over (CG_(n,t)CG_(p,t′))}∥_(L2)≥d, the region ROI_(n)is compared with another region of the image Im_(t′), if an untested onestill exists up to the present time. Thus, if p<Nt′−1 (step S306), thenp is incremented by 1 (step S308) and the method resumes at the stepS302 with the new region ROI_(p) of the image Im_(t).

Otherwise, i.e. if p≥Nt′−1 (step S306), then if |t−t′|<f (step S310),the value t′ is decremented by 1 and p is set to 0 (step S312). Themethod resumes at the step S302 with the new region ROI_(p) in the newimage Im_(t′). Otherwise, i.e. if |t−t′|≥f (step S310), the methodcontinues at the step S314. At the step S314, a new label is created andassociated with the region ROI_(n) with which regions in images thatfollow Im_(t) will be able to be associated. This is because in thiscase no segmented region in the f images that precedes it is closewithin the meaning of the predefined criterion of the region ROI_(n) ofthe image Im_(t). In a particular embodiment, a Hungarian method is usedfor guaranteeing that a plurality of ROI_(n) regions of the currentimage are not associated with an ROI_(p) of a preceding image and viceversa, and that these associations are optimum in the sense of thedistance between centres of gravity. The document by Kuhn entitled “TheHungarian method for the assignment problem” and published in 1955 inNaval Research Logistics Quarterly, 2: 83-97, describes this method,which makes it possible to find an optimum coupling of maximum (andrespectively minimum) weight in a bipartite graph the edges of which arevalued.

The stability value S_(n) for the region ROI_(n) of the image Im_(t) isequal to a percentage of occurrences, in F images preceding the imageIm_(t), of the label that is associated therewith. Thus, for the regionROI_(n) of Im_(t),

$S_{n} = \frac{Nb\_ occurrence}{F}$

where Nb_occurrence is the number of times when the label associatedwith ROI_(n) is associated with a region in the F images that precedeIm_(t).

Returning to FIG. 2, in a step S104-8, n is compared with Nt−1. Ifn<Nt−1, n is incremented by 1 in a step S104-10. Otherwise the methodcontinues at the step S104-12. During the step S104-12, a valuerepresenting a stability of the image Im_(t) is determined. It is equalto the mean of the stability values of the ROI_(n) regions of the image:

$\begin{matrix}{S = {\frac{1}{Nt}{\sum\limits_{n = 0}^{{Nt} - 1}S_{n}}}} & \left( {{Eq}.\mspace{14mu} 1} \right)\end{matrix}$

In the variant embodiment wherein, at each new iteration, the stabilityand therefore the score of the image BestImage is updated taking accountof information on the stability coming from images acquired subsequentlyto the best image, and the stability value is updated taking account ofthe occurrences of the tags associated with its regions in the followingimages. For this purpose, if the stability of a region ROI_(m) in theimage Im_(t), is lower than the stability of the region ROI_(p) in theimage Im_(t), the region ROI_(p) in the image Im_(t) having the samelabel as the region ROI_(m) in the image Im_(t′), the stabilityassociated with the region ROI_(m) in the image Im_(t), is replaced bythe stability associated with the region ROI_(p) in the image Im_(t).This is done for each of the regions that the image Im_(t′) shares withthe image Im_(t), i.e. two regions that have the same label in the twoimages. The global stability of the image Im_(t′) is updated, i.e.recalculated, taking account of the updated stabilities associated withthe regions that it comprises.

Let Im_(t′) be the current best image that has been acquired at theinstant t′. For each region present both in the image Im_(t′) and in theimage Im_(t) acquired at the current instant t, the stability valueassociated with the region ROI_(m) in the image Im_(t′) is updated asfollows:

S _(m) ^(update)(t′)=max(S _(p)(t),S _(m)(t′))

where

S_(p)(t) is the stability associated with the region ROI_(p) in theimage Im_(t);

S_(m)(t′) is the stability associated with the region ROI_(m) in theimage Im_(t′); and

S_(m) ^(update)(t′) is the stability associated with the region ROI_(m)in the image Im_(t′) after the updating.

The value representing the BestImage stability is therefore updatedusing, in the equation (Eq. 1), the values S_(m) ^(update) (t′). Inupdating the score account is taken of the value representing theupdated BestImage stability.

FIG. 4 illustrates schematically an example of performance of the stepS106.

The step S106 starts (S106-2) with n=0, by convention.

In a step S106-4, gradients of the image are determined. In a particularembodiment, the horizontal and vertical gradients are determined. In avariant, gradients in the image are determined in directions other thanthe horizontal and vertical directions. Sobel filters are for exampleused for determining the horizontal and vertical gradients.

The Gx and Gy matrices below define such Sobel filters:

${Gx} = {{\begin{pmatrix}{- 1} & 0 & 1 \\{- 2} & 0 & 2 \\{- 1} & 0 & 1\end{pmatrix}\mspace{14mu}{and}\mspace{14mu}{Gy}} = \begin{pmatrix}1 & 2 & 1 \\0 & 0 & 0 \\{- 1} & {- 2} & {- 1}\end{pmatrix}}$

In an optional step S106-6, the contours of the regions ROI_(n) areeroded by approximately a factor alpha of the width of the finger usingthe segments formed by the centre of gravity (Cx, Cy) determined atS104-4 and each of the points of the contour (xi, yi)_(i=0,m) asregression axes. Thus each point of the contour is moved on the axisthat connects it to the centre of gravity so that the length of thesegment is reduced by r, with r=alpha*l where l is the width of thefinger in the image, alpha having for example the value 0.25.

In a step S106-8, a sum Tn of the magnitudes of the gradients on thesurface of the region ROI_(n) is determined. In a variant, the sum ofthe magnitudes of the gradients on the surface of the region ROI aftererosion of the contour is determined. This variant makes it possible todetermine the value Tn on a central zone of the region ROI and thus toavoid edge effects. This is because high contrasts between thebackground and the contour of the finger will cause projecting contoursthat will increase the sum of the magnitudes of the gradients.

Tn is for example calculated as follows from the Tenengrad function fromROI_(n):

${Tn} = \sqrt{{\sum\limits_{{({x,y})} \in {ROIn}}\left( {{Gx}*{{Imt}\left( {x,y} \right)}} \right)^{2}} + \left( {{Gy}*{{Imt}\left( {x,y} \right)}} \right)^{2}}$

where * is the convolution operator.

Tn is for example calculated as follows from the Tenengrad function fromROI_(n) after erosion:

${Tn} = \sqrt{{\sum\limits_{{({x,y})} \in {{Erode}{({ROIn})}}}\left( {{Gx}*{{Imt}\left( {x,y} \right)}} \right)^{2}} + \left( {{Gy}*{{Imt}\left( {x,y} \right)}} \right)^{2}}$

In a step S106-10, n is compared with Nt−1. If n<Nt−1, n is incrementedby 1 in a step S106-12. Otherwise the method continues at a stepS106-14.

In the step S106-14, a value T representing a sharpness of the imageIm_(t) is determined. It is equal to the mean of the sum values Tn ofthe regions ROI_(n) of the image:

$T = {\frac{1}{Nt}{\sum\limits_{n = 0}^{{Nt} - 1}{Tn}}}$

The method described is advantageously used for accelerating the accesscontrol flows in stadiums and for combating fraud and the ticket blackmarket. The individual identifies/registers themself before the event,e.g. at home, by means of the camera of his smartphone, which capturesthe prints of his fingers. For this purpose, the user positions his handin front of the lens (with or without movement) of the camera. An imageis then selected using the methods described in relation to FIGS. 1 to4. The image selected will be used for their identification. On arrivingat the stadium, the individual is identified by a dedicated printsensor. The dedicated print sensor then checks whether the printcaptured corresponds to a print in the database. This proceduretherefore makes it possible to make the flows of spectators in thestadium fluid, to dispense with paper tickets and thus mitigate any lossof tickets.

The method described makes it possible to improve the performance, byapproximately 0.7%, in terms of precision of identification and toreduce the acquisition time by an order of 40%, with a negligibleincrease in the computing time. Moreover, the method is robust to achange in the image capture module, e.g. to a change of smartphone.

FIG. 5 illustrates schematically an example of a hardware architecturedevice 140 for selecting an image of at least one fingerprint forauthentication or identification. According to the example of hardwarearchitecture shown in FIG. 5, the device 140 then comprises, connectedby a communication bus 1400: a processor or CPU (central processingunit) 1401; a random access memory RAM 1402; a read only memory ROM1403; a storage unit such as a hard disk or such as a storage mediumreader, e.g. an SD (Secure Digital) card reader 1404; at least onecommunication interface 405 enabling the device 140 to send or receiveinformation.

The processor 1401 is capable of executing instructions loaded in theRAM 1402 from the ROM 1403, from an external memory (not shown), from astorage medium (such as an SD card), or from a communication network.When the device 140 is powered up, the processor 1401 is capable ofreading instructions from the RAM 1402 and executing them. Theseinstructions form a computer program causing the implementation, by theprocessor 1401, of all or some of the methods described in relation toFIGS. 1 to 4.

The methods described in relation to FIGS. 1 to 4 can be implemented insoftware form by executing a set of instructions by a programmablemachine, for example a DSP (digital signal processor) or amicrocontroller, or be implemented in hardware form by a machine or adedicated component, for example an FPGA (field-programmable gate array)or an ASIC (application-specific integrated circuit). In general, thedevice 140 comprises electronic circuitry configured for implementingthe methods described in relation to FIGS. 1 to 4.

1. A method for selecting an image of a fingerprint for identifying or authenticating an individual, said image being selected in a sequence of images acquired by an image capture module, comprising: a) acquiring an image, referred to as the current image, comprising at least one fingerprint with said image capture module and segmenting said at least one fingerprint; b) determining a value representing a stability of said current image from said at least one segmented fingerprint; c) determining a value representing a sharpness of said current image from said at least one segmented fingerprint; d) determining a score for said current image, said score being a combination of said value representing a stability, of said value representing a sharpness and of a number of segmented fingerprints in said current image; e) selecting said current image for identifying or authenticating said individual in the case where said score is higher than a first threshold value, and otherwise storing said current image in memory as the best image in the case where the score thereof is higher than a best score value, said best score value then being made equal to the score of said current image, and repeating steps a) to e), wherein determining a value representing a stability of said current image from said at least one segmented fingerprint comprises: for each segmented fingerprint, said fingerprint being segmented in the form of a region, referred to as the current region: determining a centre of gravity of said current region; determining a stability value of said current region according to a distance between the centre of gravity of said current region and the centre of gravity of at least one region determined in one of the f images preceding said current image timewise, f being an integer representing a number of images; determining a mean value of said determined stability values, said value representing a stability of said current image being equal to said mean value determined.
 2. The method according to claim 1, wherein determining a stability value of said current region comprises: determining a distance between the centre of gravity of said current region and the centre of gravity of at least one determined region in one of the f images preceding said current image timewise, referred to as the preceding region, with which a label is associated; associating said label with said current region in the case where said distance is smaller than a second threshold value; and associating a new label with said current region otherwise; determining the percentage of occurrences in F images preceding said current image of said label, F being an integer representing a number of images, said stability value of said current region being equal to said percentage.
 3. The method according to claim 1, wherein determining a stability value of said current region comprises: determining a distance between the centre of gravity of said current region and the centre of gravity of at least one determined region in one of the f images preceding said current image timewise, referred to as the preceding region, with which a label is associated; determining a first directing vector of said current region and a second directing vector of said preceding region; associating said label with said current region in the case where said distance is less than a second threshold value and a scalar product of said first directing vector and of said second directing vector is higher than a third threshold value; associating a new label with said current region otherwise; determining the percentage of occurrences in F images preceding said current image of said label, F being an integer representing a number of images, said stability value of said current region being equal to said percentage.
 4. The method according to claim 2, wherein f=0.2*fps and F=0.8*fps where fps is the number of images per second.
 5. The method according to claim 1, wherein determining a value representing the sharpness of said current image from said at least one segmented fingerprint comprises: determining gradients in said current image; for each segmented fingerprint, said fingerprint being segmented in the form of a region, determining a sum of the magnitudes of the gradients inside said region; determining a mean value of said sums determined, said value representing the sharpness of said current image being equal to said mean value determined.
 6. The method according to claim 5, wherein each region is eroded prior to said determination of a sum of the magnitudes of the gradients and said determination of a sum of the magnitudes of the gradients comprises determining a sum of the magnitudes of the gradients inside said eroded region.
 7. The method according to claim 1, wherein said score for said current image is determined according to the following formula: Score(Im _(t))=min(TH1,Stability(Im _(t)))*min(TH2,Sharpness(Im _(t)))*min(TH3,Nt) where TH1, TH2 and TH3 are threshold values, Stability(Im_(t)) is the stability value of said current image, Sharpness(Imt) is the sharpness value of said current image and Nt is the number of segmented fingerprints in said current image.
 8. The method according to claim 1, wherein the step e) further comprises updating said best score value of said image stored in memory as best image taking account of the value representing the stability of said current image; and selecting said image stored in memory as best image for identifying and authenticating said individual in the case where said updated best score value is higher than said first threshold value and than said score of said current image.
 9. The method according to claim 8, wherein updating said best score value of said image stored in memory as best image taking account of the value representing the stability of said current image comprises, for each region that said best image shares with said current image, replacing the stability associated with said region in said best image with the stability associated with said region in said current image in the case where the stability associated with said region in said current image is greater than the stability associated with said region in said best image and updating said best score value of said best image taking account of said replaced stabilities.
 10. A device for selecting an image of a fingerprint for identifying or authenticating an individual, said selection device comprises an image capture module, said image being selected in a sequence of images acquired by said image capture module, the selection device comprising electronic circuitry configured to: a) acquire an image, referred to as the current image, comprising at least one fingerprint with said image capture module and segmenting said at least one fingerprint; b) determine a value representing a stability of said current image from said at least one segmented fingerprint; c) determine a value representing a sharpness of said current image from said at least one segmented fingerprint; d) determine a score for said current image, said score being a combination of said value representing a stability, of said value representing a sharpness and of a number of segmented fingerprints in said current image; e) select said current image for identifying or authenticating said individual in the case where said score is higher than a first threshold value, and otherwise store said current image in memory as the best image in the case where the score thereof is higher than a best score value, said best score value then being made equal to the score of said current image, and repeat steps a) to e), wherein determining a value representing a stability of said current image from said at least one segmented fingerprint comprises: for each segmented fingerprint, said fingerprint being segmented in the form of a region, referred to as the current region: determining a centre of gravity of said current region; determining a stability value of said current region according to a distance between the centre of gravity of said current region and the centre of gravity of at least one region determined in one of the f images preceding said current image timewise, f being an integer representing a number of images; determining a mean value of said determined stability values, said value representing a stability of said current image being equal to said mean value determined.
 11. (canceled)
 12. A non-transitory storage medium storing instructions for implementing, by a device for selecting an image, the method according to claim 1, when said instructions are executed by a processor of said device for selecting an image. 